Development and Validation of Novel Nomograms to Predict the Overall Survival and Cancer-Specific Survival of Cervical Cancer Patients With Lymph Node Metastasis

Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

BACKGROUND

Concurrent chemoradiotherapy (CCRT) is used as the primary treatment modality for currently limited cervical cancer and lacks non-invasive quantitative parameters to assess clinical outcomes of treatment for cervical cancer treatment.

PURPOSE

To develop nomograms based on clinical prognostic factors and apparent diffusion coefficient (ADC) in predicting downstaging and progression-free survival (PFS) after CCRT for cervical cancer.

MATERIAL AND METHODS

X-tile was used to calculate the optimal threshold for ΔADCmean(%) for prognostic stratification. Kaplan-Meier curves were used to calculate the difference in PFS between high- and low-risk groups. Univariate and multivariate Cox proportional risk regression models were used to identify clinical and radiological risk factors for prognosis and construct a prognostic nomogram model.

RESULTS

ΔADCmean(%) was significantly correlated with tumor downstaging; the area under the receiver operating characteristic curve (AUC) was 0.868. X-tile showed that the optimal threshold for ΔADCmean(%) to diagnose prognosis was 40.8. Kaplan-Meier curves showed that the low-risk population in the training group had significantly longer PFS within 3 years (P < 0.001). Multivariate Cox regression showed that ΔADC (%) is independent risk factor for PFS. The C-index of ΔADC(%) predicting 3-year PFS in the training set is 0.761 and the C-index of the nomogram model is 0.862.

CONCLUSION

ΔADCmean(%) is a non-invasive biomarker for predicting tumor downstaging in cervical cancer after CCRT. The nomograms based on ΔADCmean(%) predict PFS of patients with cervical cancer with moderate accuracy.

Analysis of prognostic factors for cervical mucinous adenocarcinoma and establishment and validation a nomogram: a SEER-based study

Up to now, there are no relevant studies on prognostic factors of cervical mucinous adenocarcinoma. Therefore, we explored the prognostic factors for cervical mucinous adenocarcinoma, and established and validated the prognostic model using the SEER database. We selected the independent factors through univariate and multivariate analyses. LASSO regression analysis was conducted to identify potential risk factors. In conjunction with LASSO and multivariate analysis, the nomogram incorporated three variables, including age, tumour size, and AJCC stage for OS. The c-index was 0.794 and 0.831 in development and validated cohorts, indicating that this prediction model showed adequate discriminative ability in the development cohort. Besides, calibration curves showed good concordance for the development cohort, as well as the validation cohort. We constructed a first-of-its-kind nomogram to predict cervical mucinous adenocarcinomas OS and it showed better performance than AJCC and FIGO stages. Patients with cervical mucinous adenocarcinoma might benefit from using this model to develop tailored treatments.IMPACT STATEMENTWhat is already known on this subject? Cervical cancer has a variety of pathological types. The biological behaviour of each type is different, and the prognosis is quite different.What do the results of this study add? We analysed and explored the relevant factors affecting the prognosis of cervical mucinous adenocarcinoma.What are the implications of these findings for clinical practice and/or further research? Through the analysis of the SEER dataset, the prognostic factors affecting cervical mucinous adenocarcinoma were identified, and the first predictive model was created to predict the prognosis to help doctors develop individualised treatment plans and follow-up plans.

Characteristics of lactate metabolism phenotype in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer, and more effective prognostic markers are needed. Lactic acid has been proved to be an important metabolite involved in cancer development, metastasis, and the tumor microenvironment, affecting the prognosis of patients. The role of lactic acid metabolism regulators (LAMRs) in HCC is still unclear. In this study, we analyzed the status of LAMRs, a gene list containing lactate from Molecular Signatures database, in HCC and consensus clustering was performed based on these LAMRs. Cluster B showed higher infiltrations of immune cells, higher TME scores, and a poorer prognosis. We further constructed a risk score based on DEGs using LASSO and COX regression analysis between two clusters, which could effectively predict the prognosis of TCGA-LIHC patients. The GSE14520 cohort confirmed the result. We also examined the correlation of risk scores with clinical characteristics, genetic mutations, drug sensitivity, immune checkpoint inhibitors(ICIs), and immunotherapy. In conclusion, our findings will facilitate a further understanding of the role of partial lactate metabolism related genes in HCC and suggest a new risk score to predict prognosis.

Establishment of prediction models to predict survival among patients with cervical cancer based on socioeconomic factors: a retrospective cohort study based on the SEER Database

OBJECTIVE

To construct and validate predictive models based on socioeconomic factors for predicting overall survival (OS) in cervical cancer and compare them with the American Joint Council on Cancer (AJCC) staging system.

DESIGN

Retrospective cohort study.

SETTING AND PARTICIPANTS

We extracted data from 5954 patients who were diagnosed with cervical cancer between 2007 and 2011 from the Surveillance, Epidemiology, and End Results Database. This database holds data related to cancer incidence from 18 population-based cancer registries in the USA.

OUTCOME MEASURES

1-year and 5-year OS.

RESULTS

Of the total 5954 patients, 5820 patients had 1-year mortality and 5460 patients had 5-year mortality. Lower local education level [Hazard ratios (HR): 1.15, 95% confidence interval (CI): 1.04 to 1.27, p= 0.005] and being widowed (HR 1.28, 95% CI 1.06 to 1.55, p=0.009) were associated with a worse OS for patients with cervical cancer. Having insurance (HR 0.75, 95% CI 0.62 to 0.90, p=0.002), earning a local median annual income of ≥US$56 270 (HR 0.83, 95% CI 0.75 to 0.92, p<0.001) and being married (HR 0.79, 95% CI 0.69 to 0.89, p<0.001) were related to better OS in patients with cervical cancer. The predictive models based on socioeconomic factors and the AJCC staging system had a favourable performance for predicting OS in cervical cancer compared with the AJCC staging system alone.

CONCLUSION

Our proposed predictive models exhibit superior predictive performance, which may highlight the potential clinical application of incorporating socioeconomic factors in predicting OS in cervical cancer.

Development and validation of a nomogram for predicting pelvic lymph node metastasis and prognosis in patients with cervical cancer

Objective

Cervical cancer (CC) is one of the main causes of death among gynecological malignancies. Patients with CC with lymph node metastasis (LNM) have poor prognoses. We investigated the risk factors and prognosis of LNM in patients with CC patients using data from the SEER database.

Methods

We collected the information of cervical cancer patients registered in SEER database from 2010 to 2015. The dataset was divided into a training set and a validation set at a 7:3 ratio. LASSO regression analysis was used to evaluate risk factors for LNM in patients with CC. Using the results, we established a nomogram prediction model. C-index, ROC curves, calibration curves, decision curve analysis, and clinical impact curves were used to evaluate the prediction performance of the model.

Results

We included 14,356 patients with CC in the analysis. Among these, 3997 patients were diagnosed with LNM. A training set (10,050 cases) and a validation set (4306 cases) were used for the following analysis. We established nomogram LNM prediction models for the patients with T_1-2-stage CC. The C-indices for the internal and external validations of the prediction models were 0.758 and 0.744, respectively. In addition, we established a prognostic nomogram for all CC patients with LNM, and the internal and external validation C-indices were 0.763 and 0.737.

Conclusion

We constructed a quantitative and visual predictive nomogram that predicted prognosis of patients with LNM in CC to provide clinicians with a reference for diagnosis and treatment.